In [21]:
import numpy as np
import pandas as pd
import plotly.offline as pyo
import plotly.graph_objects as go
pyo.init_notebook_mode()

dfEconomy = pd.read_csv('Modified Data/EconomicIndicators.csv')
In [9]:
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    display(dfEconomy)
Date GDP (current US$) GDP (constant 2015 US$) GDP growth (annual %) GDP per capita (current US$) GDP per capita (constant 2015 US$) GDP per capita growth (annual %) GDP, PPP (current international $) GDP, PPP (constant 2017 international $) GNI (current US$) GNI (constant 2015 US$) GNI growth (annual %) Exports of goods and services (BoP, current US$) Exports of goods and services (constant 2015 US$) Exports of goods and services (annual % growth) Imports of goods and services (BoP, current US$) Imports of goods and services (constant 2015 US$) Imports of goods and services (annual % growth) Inflation, consumer prices (annual %) External debt stocks, total (DOD, current US$) External debt (% of GDP) GDP, PPP Growth (annual %)
0 1960 3.749265e+09 1.705191e+10 NaN 81.586947 371.062860 NaN NaN NaN 3.743806e+09 1.702708e+10 NaN 3.711538e+08 1.688032e+09 NaN 2.485962e+09 1.130632e+10 NaN 6.947368 NaN NaN NaN
1 1961 4.118648e+09 1.807286e+10 5.987346 87.517372 384.031276 3.494938 NaN NaN 4.113188e+09 1.804891e+10 6.001208 3.962018e+08 1.738556e+09 2.993079 2.703655e+09 1.186379e+10 4.930638 1.640420 NaN NaN NaN
2 1962 4.310164e+09 1.888304e+10 4.482859 89.493336 392.074799 2.094497 NaN NaN 4.304914e+09 1.886004e+10 4.494103 4.090443e+08 1.792044e+09 3.076539 2.729544e+09 1.195827e+10 0.796334 -0.516462 NaN NaN NaN
3 1963 4.630827e+09 2.052376e+10 8.688832 93.883886 416.092027 6.125675 NaN NaN 4.618647e+09 2.046978e+10 8.535162 5.619018e+08 2.490341e+09 38.966518 3.176099e+09 1.407642e+10 17.712905 1.456488 NaN NaN NaN
4 1964 5.204956e+09 2.207736e+10 7.569757 102.961207 436.720611 4.957698 NaN NaN 5.191096e+09 2.201857e+10 7.566232 5.723116e+08 2.427519e+09 -2.522625 3.537682e+09 1.500545e+10 6.599837 4.179587 NaN NaN NaN
5 1965 5.929231e+09 2.437768e+10 10.419366 114.372019 470.233701 7.673805 NaN NaN 5.904242e+09 2.427494e+10 10.247571 5.937100e+08 2.441003e+09 0.555479 4.105651e+09 1.688014e+10 12.493420 5.568635 NaN NaN NaN
6 1966 6.561109e+09 2.578914e+10 5.789952 123.330471 484.763527 3.089916 NaN NaN 6.530659e+09 2.566945e+10 5.744658 6.875562e+08 2.702513e+09 10.713203 3.989464e+09 1.568101e+10 -7.103777 7.227622 NaN NaN NaN
7 1967 7.464511e+09 2.718191e+10 5.400613 136.638093 497.565621 2.640895 NaN NaN 7.414512e+09 2.699984e+10 5.182761 7.780730e+08 2.833341e+09 4.841005 4.900859e+09 1.784641e+10 13.809021 6.811400 NaN NaN NaN
8 1968 8.041999e+09 2.914803e+10 7.233221 143.287946 519.343746 4.376935 NaN NaN 7.976001e+09 2.890882e+10 7.070367 8.342945e+08 3.023881e+09 6.724890 5.249102e+09 1.902524e+10 6.605467 0.170627 NaN NaN NaN
9 1969 8.683116e+09 3.075348e+10 5.507900 150.547804 533.203577 2.668720 NaN NaN 8.608118e+09 3.048785e+10 5.462100 9.311302e+08 3.297836e+09 9.059728 4.491420e+09 1.590751e+10 -16.387338 3.186987 NaN NaN NaN
10 1970 1.002751e+10 3.424506e+10 11.353462 169.124000 577.577323 8.322102 NaN NaN 9.933511e+09 3.392405e+10 11.270708 1.118591e+09 3.820114e+09 15.836999 6.893466e+09 2.354196e+10 47.992689 5.349841 3.406743e+09 33.973965 NaN
11 1971 1.066590e+10 3.440546e+10 0.468373 175.198920 565.146954 -2.152157 NaN NaN 1.060271e+10 3.420163e+10 0.818254 1.194777e+09 3.854044e+09 0.888187 6.567159e+09 2.118398e+10 -10.016073 4.730691 3.795845e+09 35.588613 NaN
12 1972 9.415016e+09 3.468531e+10 0.813406 150.617211 554.880102 -1.816669 NaN NaN 9.280297e+09 3.418900e+10 -0.036931 8.663720e+08 3.191751e+09 -17.184375 3.920867e+09 1.444464e+10 -31.813375 5.183238 4.072099e+09 43.251106 NaN
13 1973 6.383429e+09 3.713558e+10 7.064264 99.297932 577.665326 4.106333 NaN NaN 6.304784e+09 3.667805e+10 7.280269 5.801770e+08 3.375178e+09 5.746911 2.204229e+09 1.282309e+10 -11.225944 23.070084 4.566896e+09 71.542984 NaN
14 1974 8.899192e+09 3.845025e+10 3.540192 134.532180 581.265753 0.623272 NaN NaN 8.825592e+09 3.813225e+10 3.964748 6.462350e+08 2.792152e+09 -17.273927 3.431544e+09 1.482648e+10 15.623299 26.663035 5.121326e+09 57.548218 NaN
15 1975 1.123061e+10 4.006955e+10 4.211416 164.848096 588.159566 1.186000 NaN NaN 1.113531e+10 3.972953e+10 4.188789 8.729413e+08 3.114557e+09 11.546830 3.891331e+09 1.388383e+10 -6.357856 20.904509 5.752877e+09 51.224991 NaN
16 1976 1.316808e+10 4.213561e+10 5.156190 187.496907 599.958055 2.006001 NaN NaN 1.302388e+10 4.167419e+10 4.894763 1.449756e+09 3.555345e+09 14.152517 2.643865e+09 1.485010e+10 6.959644 7.158324 6.802352e+09 51.657882 NaN
17 1977 1.512606e+10 4.379899e+10 3.947698 208.776120 604.531768 0.762339 NaN NaN 1.494521e+10 4.327532e+10 3.842014 1.438288e+09 2.930241e+09 -17.582100 2.993491e+09 1.643440e+10 10.668627 10.132968 7.564124e+09 50.007233 NaN
18 1978 1.781152e+10 4.732417e+10 8.048534 238.155833 632.766351 4.670488 NaN NaN 1.763026e+10 4.684258e+10 8.243173 1.805675e+09 3.304059e+09 12.757248 3.855733e+09 1.755601e+10 6.824757 6.138693 8.329289e+09 46.763506 NaN
19 1979 1.968838e+10 4.910282e+10 3.758436 254.347761 634.343194 0.249198 NaN NaN 1.945476e+10 4.852016e+10 3.581310 2.503913e+09 3.743810e+09 13.309419 5.100890e+09 2.286609e+10 30.246484 8.267047 8.918891e+09 45.300268 NaN
20 1980 2.365444e+10 5.411902e+10 10.215704 293.391890 671.251494 5.818349 NaN NaN 2.337231e+10 5.347352e+10 10.208870 3.232901e+09 4.474111e+09 19.506883 6.347873e+09 2.378178e+10 4.004601 11.938231 9.931199e+09 41.984493 NaN
21 1981 2.810061e+10 5.840566e+10 7.920764 333.458392 693.076044 3.251322 NaN NaN 2.783846e+10 5.786081e+10 8.204600 3.389497e+09 5.290494e+09 18.246841 6.601898e+09 1.881328e+10 -20.892062 11.879914 1.058077e+10 37.653165 NaN
22 1982 3.072597e+10 6.222392e+10 6.537487 349.841762 708.473153 2.221561 NaN NaN 3.040362e+10 6.157111e+10 6.412473 3.154730e+09 4.973868e+09 -5.984815 6.721840e+09 1.872425e+10 -0.473207 5.903529 1.170399e+10 38.091535 NaN
23 1983 2.869189e+10 6.644169e+10 6.778378 315.017266 729.484209 2.965681 NaN NaN 2.826895e+10 6.546229e+10 6.319800 3.662621e+09 6.196074e+09 24.572534 6.607695e+09 2.080395e+10 11.106973 6.362033 1.202622e+10 41.915062 NaN
24 1984 3.115183e+10 6.980710e+10 5.065206 331.388766 742.598190 1.797706 NaN NaN 3.070697e+10 6.881024e+10 5.114330 3.286531e+09 5.967308e+09 -3.692100 7.355744e+09 2.230861e+10 7.232582 6.087167 1.222789e+10 39.252547 NaN
25 1985 3.114492e+10 7.510694e+10 7.592115 320.679810 773.329248 4.138316 NaN NaN 3.063643e+10 7.388069e+10 7.368739 3.509102e+09 5.945824e+09 -0.360031 7.090127e+09 2.429777e+10 8.916550 5.614839 1.346488e+10 43.232996 NaN
26 1986 3.189907e+10 7.923906e+10 5.501654 317.029798 787.519613 1.834971 NaN NaN 3.125540e+10 7.749968e+10 4.898425 4.035983e+09 7.896553e+09 32.808379 7.200329e+09 2.369972e+10 -2.461337 3.506414 1.495441e+10 46.880387 NaN
27 1987 3.335153e+10 8.435184e+10 6.452343 319.915392 809.121840 2.743072 NaN NaN 3.265316e+10 8.241636e+10 6.344130 4.928011e+09 8.843321e+09 11.989638 7.570955e+09 2.417180e+10 1.991929 4.681219 1.679767e+10 50.365517 NaN
28 1988 3.847274e+10 9.078390e+10 7.625279 356.335215 840.842056 3.920326 NaN NaN 3.763885e+10 8.864327e+10 7.555432 5.282184e+09 8.461946e+09 -4.312569 8.623660e+09 2.334876e+10 -3.404947 8.837937 1.706517e+10 44.356508 NaN
29 1989 4.017102e+10 9.528657e+10 4.959769 359.728480 853.284151 1.479718 NaN NaN 3.928197e+10 9.301877e+10 4.936072 6.005657e+09 9.627554e+09 13.774694 9.112948e+09 2.529190e+10 8.322199 7.844265 1.834819e+10 45.675190 NaN
30 1990 4.001042e+10 9.953500e+10 4.458587 346.668516 862.416565 1.070266 2.327968e+11 3.533440e+11 3.903798e+10 9.693854e+10 4.213955 6.834726e+09 9.735861e+09 1.124974 1.020537e+10 2.440678e+10 -3.499598 9.052132 2.066338e+10 51.644981 NaN
31 1991 4.562523e+10 1.045730e+11 5.061568 382.750576 877.264310 1.721644 2.528511e+11 3.712288e+11 4.445642e+10 1.017412e+11 4.954291 7.941736e+09 1.299399e+10 33.465230 1.099746e+10 2.260143e+10 -7.396928 11.791270 2.336332e+10 51.207007 5.061568
32 1992 4.888461e+10 1.126313e+11 7.705898 399.465049 920.377228 4.914473 2.785418e+11 3.998353e+11 4.760841e+10 1.095159e+11 7.641726 8.472574e+09 1.478989e+10 13.820981 1.239996e+10 2.957242e+10 30.843125 9.509041 2.491962e+10 50.976410 7.705898
33 1993 5.180995e+10 1.146111e+11 1.757748 412.675000 912.896796 -0.812757 2.901563e+11 4.068634e+11 5.029901e+10 1.110863e+11 1.433939 8.366369e+09 1.498472e+10 1.317358 1.201866e+10 3.396225e+10 14.844358 9.973665 2.455156e+10 47.387735 1.757748
34 1994 5.229346e+10 1.188946e+11 3.737416 404.606760 919.915416 0.768829 3.074283e+11 4.220696e+11 5.068341e+10 1.150187e+11 3.539918 8.869456e+09 1.545085e+10 3.110663 1.188476e+10 3.029795e+10 -10.789340 12.368194 2.739083e+10 52.379071 3.737416
35 1995 6.063602e+10 1.247949e+11 4.962609 455.507603 937.479237 1.909287 3.294512e+11 4.430152e+11 5.885066e+10 1.208556e+11 5.074791 1.021360e+10 1.497568e+10 -3.075315 1.418530e+10 3.150071e+10 3.969777 12.343579 3.024109e+10 49.873137 4.962609
36 1996 6.332012e+10 1.308432e+11 4.846581 461.399865 953.425408 1.700963 3.517428e+11 4.644863e+11 6.135301e+10 1.264826e+11 4.655964 1.052348e+10 1.527438e+10 1.994558 1.562253e+10 3.578274e+10 13.593456 10.373809 2.985095e+10 47.142919 4.846581
37 1997 6.243330e+10 1.321704e+11 1.014396 441.754634 935.188373 -1.912791 3.614380e+11 4.691980e+11 6.024700e+10 1.272020e+11 0.568747 9.975895e+09 1.427605e+10 -6.535973 1.340845e+10 3.442828e+10 -3.785257 11.375493 3.009512e+10 48.203642 1.014396
38 1998 6.219196e+10 1.355411e+11 2.550234 427.506327 931.706805 -0.372285 3.748273e+11 4.811637e+11 5.985318e+10 1.298858e+11 2.109860 9.155000e+09 1.345804e+10 -5.729972 1.199600e+10 3.249302e+10 -5.621130 6.228004 3.231019e+10 51.952370 2.550234
39 1999 6.297386e+10 1.405021e+11 3.660133 420.682602 938.592245 0.739014 3.940220e+11 4.987749e+11 6.116586e+10 1.360770e+11 4.766671 8.945000e+09 1.307444e+10 -2.850317 1.156500e+10 3.073793e+10 -5.401426 4.142637 3.419227e+10 54.295981 3.660133
40 2000 8.201774e+10 1.464876e+11 4.260088 531.306496 948.938556 1.102322 4.201147e+11 5.200232e+11 7.999774e+10 1.425981e+11 4.792214 9.997000e+09 1.516845e+10 16.016001 1.202600e+10 3.004631e+10 -2.250047 4.366665 3.311199e+10 40.371739 4.260088
41 2001 7.948440e+10 1.516944e+11 3.554418 499.218306 952.747891 0.401431 4.448488e+11 5.385070e+11 7.732440e+10 1.472903e+11 3.290492 1.047100e+10 1.701566e+10 12.177984 1.195200e+10 3.069417e+10 2.156206 3.148261 3.204618e+10 40.317568 3.554418
42 2002 7.990499e+10 1.554994e+11 2.508338 489.425527 952.448161 -0.031460 4.631141e+11 5.520145e+11 7.758599e+10 1.506694e+11 2.294197 1.215800e+10 1.870942e+10 9.954177 1.256600e+10 3.162925e+10 3.046429 3.290345 3.396703e+10 42.509281 2.508338
43 2003 9.176054e+10 1.644826e+11 5.777034 549.870377 985.653665 3.486332 4.995364e+11 5.839046e+11 8.954954e+10 1.602760e+11 6.375903 1.477500e+10 2.401902e+10 28.379245 1.521000e+10 3.517791e+10 11.219542 2.914135 3.666857e+10 39.961154 5.777034
44 2004 1.077597e+11 1.768959e+11 7.546860 631.471171 1036.608932 5.169693 5.516573e+11 6.279711e+11 1.055527e+11 1.730812e+11 7.989505 1.602701e+10 2.365229e+10 -1.526817 2.200700e+10 3.216049e+10 -8.577605 7.444625 3.656194e+10 33.929147 7.546860
45 2005 1.200553e+11 1.884273e+11 6.518778 688.500588 1080.604844 4.244215 6.060453e+11 6.689071e+11 1.176693e+11 1.845085e+11 6.602257 1.910500e+10 2.642603e+10 11.727160 2.927520e+10 4.487250e+10 39.526807 9.063327 3.426005e+10 28.536889 6.518778
46 2006 1.372641e+11 1.995426e+11 5.898984 770.843339 1120.585448 3.699836 6.615990e+11 7.083658e+11 1.345971e+11 1.955644e+11 5.992102 2.054000e+10 2.903976e+10 9.890712 3.509800e+10 5.325585e+10 18.682598 7.921084 3.743078e+10 27.269174 5.898984
47 2007 1.523857e+11 2.091862e+11 4.832817 837.631538 1149.851392 2.611666 7.123169e+11 7.425999e+11 1.488037e+11 2.041747e+11 4.402776 2.194600e+10 2.947818e+10 1.509728 3.758600e+10 5.108819e+10 -4.070269 7.598684 4.230609e+10 27.762503 4.832817
48 2008 1.700778e+11 2.127453e+11 1.701405 914.731489 1144.210357 -0.490588 7.383305e+11 7.552345e+11 1.661548e+11 2.078602e+11 1.805072 2.547250e+10 2.813631e+10 -4.552083 4.792900e+10 5.408293e+10 5.861904 20.286121 4.982630e+10 29.296179 1.701405
49 2009 1.681528e+11 2.187695e+11 2.831659 884.441014 1150.672105 0.564734 7.641038e+11 7.766202e+11 1.637458e+11 2.130407e+11 2.492313 2.231300e+10 2.719060e+10 -3.361179 3.515143e+10 4.548065e+10 -15.905721 13.647765 5.666292e+10 33.697286 2.831659
50 2010 1.771656e+11 2.222844e+11 1.606689 911.090445 1143.117978 -0.656497 7.857111e+11 7.890980e+11 1.738836e+11 2.181474e+11 2.397036 2.805600e+10 3.146160e+10 15.707660 4.001600e+10 4.745768e+10 4.346972 12.938871 6.312425e+10 35.630074 1.606689
51 2011 2.135874e+11 2.283937e+11 2.748406 1075.450496 1150.002831 0.602287 8.240791e+11 8.107856e+11 2.105704e+11 2.251278e+11 3.199846 3.143300e+10 3.220793e+10 2.372198 4.715100e+10 4.740107e+10 -0.119291 11.916093 6.474256e+10 30.311974 2.748406
52 2012 2.243836e+11 2.364036e+11 3.507033 1109.679115 1169.123158 1.662633 8.470947e+11 8.392202e+11 2.211386e+11 2.329939e+11 3.494090 3.137400e+10 2.737647e+10 -15.000842 4.890200e+10 4.593374e+10 -3.095572 9.682352 6.366952e+10 28.375298 3.507033
53 2013 2.312186e+11 2.467969e+11 4.396457 1126.041261 1201.908371 2.804257 8.833753e+11 8.761161e+11 2.275496e+11 2.429033e+11 4.253064 3.004300e+10 3.109442e+10 13.580834 4.916700e+10 4.676518e+10 1.810099 7.692156 6.008782e+10 25.987455 4.396457
54 2014 2.443609e+11 2.583340e+11 4.674708 1173.392454 1240.489561 3.209994 9.317307e+11 9.170720e+11 2.404059e+11 2.541608e+11 4.634549 3.059400e+10 3.063417e+10 -1.480176 5.114100e+10 4.688412e+10 0.254337 7.189384 6.420272e+10 26.273730 4.674708
55 2015 2.705561e+11 2.705561e+11 4.731147 1282.443153 1282.443153 3.382019 9.815578e+11 9.604600e+11 2.659571e+11 2.659571e+11 4.641291 2.860400e+10 2.869089e+10 -6.343528 4.862200e+10 4.613053e+10 -1.607358 2.529328 6.861419e+10 25.360426 4.731147
56 2016 3.136299e+11 2.855091e+11 5.526736 1468.821421 1337.123374 4.263754 1.010730e+12 1.013542e+12 3.082849e+11 2.805833e+11 5.499429 2.686921e+10 2.823088e+10 -1.603304 5.190611e+10 5.352149e+10 16.021858 3.765119 7.505215e+10 23.930166 5.526736
57 2017 3.392056e+11 2.981646e+11 4.432626 1567.640986 1377.969674 3.054789 1.058469e+12 1.058469e+12 3.341916e+11 2.936706e+11 4.664352 2.949144e+10 2.892856e+10 2.471341 6.449806e+10 6.368181e+10 18.983621 4.085374 9.166207e+10 27.022568 4.432626
58 2018 3.561282e+11 3.165068e+11 6.151703 1620.742857 1440.425395 4.532445 1.150425e+12 1.123582e+12 3.506912e+11 3.115869e+11 6.100812 3.077489e+10 3.183317e+10 10.040608 6.842288e+10 7.370106e+10 15.733289 5.078057 9.922396e+10 27.861863 6.151703
59 2019 3.209095e+11 3.244120e+11 2.497637 1437.165907 1452.851573 0.862674 1.200250e+12 1.151645e+12 3.152995e+11 3.186270e+11 2.259429 3.067015e+10 3.602634e+10 13.172326 5.797608e+10 7.931113e+10 7.611928 10.578362 1.078829e+11 33.617870 2.497637
60 2020 3.004257e+11 3.202787e+11 -1.274087 1322.315036 1409.697601 -2.970295 1.199240e+12 1.136973e+12 2.949667e+11 3.142941e+11 -1.359869 2.733307e+10 3.657228e+10 1.515410 5.209794e+10 7.529519e+10 -5.063519 9.739993 1.156953e+11 38.510473 -1.274087
61 2021 3.482625e+11 3.410555e+11 6.487087 1505.010193 1473.864900 4.551848 1.330101e+12 1.210729e+12 3.438625e+11 3.365756e+11 7.089386 3.556588e+10 3.895724e+10 6.521199 7.639213e+10 8.620243e+10 14.485972 9.496211 1.304331e+11 37.452508 6.487087
62 2022 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.856314e+10 NaN NaN 7.589625e+10 NaN NaN 19.873860 NaN NaN NaN

GDP and GNI¶

In [10]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP (current US$)'], name= 'GDP', line={'color': 'blue', 'width': 2}))
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GNI (current US$)'], name= 'GNI', line={'color': '#064d02', 'width': 2}))
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP (constant 2015 US$)'], name= 'GDP (Constant 2015)', line={'color': 'cyan', 'width': 2}))
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GNI (constant 2015 US$)'], name= 'GNI (Constant 2015)', line={'color': '#90ee90', 'width': 2}))
fig.update_layout(
    title="Nominal and Constant GDP and GNI",
    xaxis_title="Year",
    yaxis_title="Value in USD $")
fig.show()
In [11]:
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP per capita (current US$)'], name='GDP per capita', line={'color': 'blue', 'width': 2}))
fig2.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP per capita (constant 2015 US$)'], name='GDP per capita (Constant 2015)', line={'color': 'cyan', 'width': 2}))
fig2.update_layout(
    title="Nominal and Constant GDP per capita",
    xaxis_title="Year",
    yaxis_title="Value in USD $"
)
fig2.show()
In [12]:
fig3 = go.Figure()
fig3.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP growth (annual %)'], name='GDP', mode='lines+markers', line={'color': 'blue', 'width': 2}))
fig3.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP per capita growth (annual %)'], name='GDP per capita', mode='lines+markers', line={'color': 'cyan', 'width': 2}))
fig3.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GNI growth (annual %)'], name='GNI', mode='lines+markers', line={'color': 'green', 'width': 2}))
fig3.update_layout(
    title="Nominal and Constant GDP and GNI Growth Rates",
    xaxis_title="Year",
    yaxis_title="Percentage %"
)
fig3.show()
In [13]:
fig4 = go.Figure()
fig4.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP, PPP (current international $)'], name='GDP PPP', line={'color': '#54518a', 'width': 2}))
fig4.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP, PPP (constant 2017 international $)'], name='GDP PPP (Constant 2017)', line={'color': '#050157', 'width': 2}))
fig4.update_layout(
    xaxis = dict(
        tickmode = 'linear',
        range = [1990, 2022],
        dtick = 5
    ),
    yaxis = dict(
        tickmode = 'linear',
        range = [0, 1400000000000],
        dtick = 100000000000,
        separatethousands= True
    ),
    title="Nominal and Constant GDP based on Purchasing Power Parity",
    xaxis_title="Year",
    yaxis_title="Value in USD $"
)
fig4.show()
In [14]:
fig5 = go.Figure()
fig5.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP, PPP Growth (annual %)'], name='GDP', mode='lines+markers', line={'color': '#54518a', 'width': 2}))
fig5.update_layout(
    xaxis = dict(
        tickmode = 'linear',
        range = [1990, 2022],
        dtick = 5
    ),
    title="GDP PPP Growth Rate",
    xaxis_title="Year",
    yaxis_title="Percentage %"
)
fig5.show()

Debt and Inflation¶

In [15]:
fig6 = go.Figure()
fig6.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['External debt stocks, total (DOD, current US$)'], name='External Debt', line={'color': 'red', 'width': 2}))
fig6.update_layout(
    xaxis = dict(
        tickmode = 'linear',
        range = [1970, 2022],
        dtick = 10
    ),
    title="External Debt (Nominal)",
    xaxis_title="Year",
    yaxis_title="Value in USD $"
)
fig6.show()
In [16]:
fig7 = go.Figure()
fig7.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['External debt (% of GDP)'], name='External Debt', line={'color': 'red', 'width': 2}))
fig7.update_layout(
    xaxis = dict(
        tickmode = 'linear',
        range = [1970, 2022],
        dtick = 10
    ),
    title="External Debt % of GDP",
    xaxis_title="Year",
    yaxis_title="Percentage %"
)
fig7.show()
In [17]:
fig8 = go.Figure()
fig8.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Inflation, consumer prices (annual %)'], name= 'Inflation', line={'color': 'firebrick', 'width': 2}))
fig8.update_layout(
    title="Inflation of PKR",
    xaxis_title="Year",
    yaxis_title="Percentage %")
fig8.show()

Imports and Exports¶

In [18]:
fig9 = go.Figure()
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Exports of goods and services (BoP, current US$)'], name= 'Exports', line={'color': '#067d00', 'width': 2}))
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Exports of goods and services (constant 2015 US$)'], name= 'Exports (Constant 2015)', line={'color': '#1c421b', 'width': 2}))
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Imports of goods and services (BoP, current US$)'], name= 'Imports', line={'color': '#d4b361', 'width': 2}))
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Imports of goods and services (constant 2015 US$)'], name= 'Imports (Constant 2015)', line={'color': '#9c8141', 'width': 2}))
fig9.update_layout(
    title="Nominal and Constant Exports and Imports",
    xaxis_title="Year",
    yaxis_title="Value in USD $")
fig9.show()
In [19]:
fig10 = go.Figure()
fig10.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Exports of goods and services (annual % growth)'], name= 'Exports', line={'color': '#1c421b', 'width': 2}))
fig10.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Imports of goods and services (annual % growth)'], name= 'Imports', line={'color': '#9c8141', 'width': 2}))
fig10.update_layout(
    title="Exports and Imports Growth Rate",
    xaxis_title="Year",
    yaxis_title="Percentage %")
fig10.show()